cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Iswanto
Contact Email
-
Phone
+628995023004
Journal Mail Official
jrc@umy.ac.id
Editorial Address
Kantor LP3M Gedung D Kampus Terpadu UMY Jl. Brawijaya, Kasihan, Bantul, Yogyakarta 55183
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 708 Documents
 57   58   59   60   61 
Predicting SI Engine Performance Using Deep Learning with CNNs on Time-Series Data Hofny, Mohamed S.; Ghazaly, Nouby M.; Shmroukh, Ahmed N.; Abouelsoud, Mostafa
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22558

Abstract

In this study, deep learning (DL) model is used to predict brake power (BP) of GX35-OHC 4-stroke, air-cooled, single-cylinder gasoline engine. The engine uses E15 (85% gasoline + 15% ethanol) as a fuel due to its high performance and low emissions. A convolutional neural networks (CNN) model is used on time-series data due to their ability to capture temporal patterns and relationships in sequential data, such as engine BP. While studying the performance of the network, it is found that the root mean squared error (RMSE) is 0.0007, explained variance score (EVS) is 0.9999, and mean absolute percentage error (MAPE) is 0.22%. Compared to traditional machine leaning methods, these metrics demonstrate the high accuracy and reliability of the model, confirming its effectiveness in predicting BP. Various performance curves are plotted such as comparing target and predicted values, regression plots (to indicate the generalization capability),  learning curve (to demonstrate the model's effective training progress and convergence), Bland-Altman plot (to show the convergence between the actual and predicted values), histogram and density plot (to show a close fit between predicted and actual values), density plot of actual and predicted outputs, and residual plot (to show randomly distributed errors). This high accuracy and reliability of this DL model help in effective real-time engine performance monitoring, and reducing emission levels, especially for the adoption and use of renewable fuels like E15.
Three-Dimensional Coordination Control of Multi-UAV for Partially Observable Multi-Target Tracking Maynad, Vincentius Charles; Nugraha, Yurid Eka; Alkaff, Abdullah
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22560

Abstract

This research deals with multi-UAV systems to track partially observable multi-targets in noisy three-dimensional environments, which are commonly encountered in defense and surveillance systems. It is a far extension from previous research which focused mainly on two-dimensional, fully observable, and/or perfect measurement settings. The targets are modeled as linear time-invariant systems with Gaussian noise and the pursuers UAV are represented in a standard six-degree-of-freedom model. Necessary equations to describe the relationship between observations regarding the target and the pursuers states are derived and represented as the Gauss-Markov model. Partially observable targets require the pursuers to maintain belief values for target positions. In the presence of a noisy environment, an extended Kalman filter is used to estimate and update those beliefs. A Decentralized Multi-Agent Reinforcement Learning (MARL) algorithm known as soft Double Q-Learning is proposed to learn the coordination control among the pursuers. The algorithm is enriched with an entropy regulation to train a certain stochastic policy and enable interactions among pursuers to foster cooperative behavior. The enrichment encourages the algorithm to explore wider and unknown search areas which is important for multi-target tracking systems. The algorithm was trained before it was deployed to complete several scenarios. The experiments using various sensor capabilities showed that the proposed algorithm had higher success rates compared to the baseline algorithm. A description of the many distinctions between two-dimensional and three-dimensional settings is also provided.
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Design of a Control System for Hybrid Quadcopter Tilt Rotor Based on Backward Transition Algorithm Darwito, Purwadi Agus; Agustina, Nilla Perdana; Ahnaf, Hudzaifa Dhiaul; Roosydi, Syahrizal Faried; Pratama, Detak Yan; Biyanto, Totok Ruki
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.22594

Abstract

An Unmanned Aerial Vehicle (UAV) is an unmanned aerial vehicle that can be controlled using either automatic or manual control. UAVs are divided into two types: rotary-wing, which uses rotating propellers to fly the aircraft, and fixed-wing, which uses fixed wings to fly the aircraft. One of the advanced developments in UAV technology is the Hybrid Vertical Take-Off Landing Quadrotor Tiltrotor Aircraft (QTRA) system, which combines the quadrotor UAV system, classified under rotary-wing, with the fixed-wing UAV system. This allows for vertical takeoff and landing as well as the ability to cruise at maximum speed. In the transition between flight modes, from quadcopter to fixed-wing and vice versa, the transition is carried out by changing the thrust direction of the two front UAV rotors from horizontal to vertical and vice versa. The change in thrust angle on the rotor is referred to as a tilt rotor. The problem that arises from changing the aircraft mode from fixed-wing to quadcopter is controlling the UAV's transition mode, which must not lose its lift force. Therefore, the tilt angle must be changed as quickly as possible. To solve this issue, a Hybrid VTOL Quadrotor Tiltrotor aircraft concept was designed with fast response, controlled by a Proportional Derivative (PD) controller. The results of the PD control system response were tested in simulations by observing the X and Z positions of the UAV, which can stabilize the position during the transition. The success criteria targeted for a stable response include a tilting angle with a settling time of 7 seconds, an overshoot height of 16 meters, and a steady-state error approaching zero. From the transition simulation tests, the system response data showed performance with an X-axis settling time of 37 seconds, a steady-state error value of 0.1 meters, and an overshoot of 0.4%.
Active Vibration Isolation using Tilt Horizontal Coupling Immune Inertial Double Link Sensor for Low Frequency Applications Nair, Vishnu G.; Hegde, Navya Thirumaleswar; V., Dileep M.
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22595

Abstract

Addressing the challenge of horizontal tilt coupling is crucial for using inertial sensors in precise applications, such as seismology and seismic isolation, including gravitational wave detection. Researchers have proposed various design solutions, with the Double Link (DL) sensor standing out for its sim- plicity, precision, and effectiveness. This paper explores the use of the DL sensor in an active vibration isolation system. We evaluated different control algorithms, including Proportional- Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and H-infinity. Simulations conducted in the Simscape environment showed that the H-infinity controller performed best, achieving a significant reduction in vibration. While the current study is based on simulations, future work will focus on experimental validation to confirm the system’s practical applicability and robustness in real-world scenarios. Our results demonstrate the potential of the DL sensor and LQG controller to enhance vibration isolation in low-frequency applications. Additionally, we conducted a detailed literature review on various methods used in similar applications. This review highlights alternative approaches, such as other sensor designs and control strategies, and discusses their advantages and limitations.
A Scoping Review on Unmanned Aerial Vehicles in Disaster Management: Challenges and Opportunities Nair, Vishnu G.; D'Souza, Jeane Marina; C. S., Asha; Rafikh, Rayyan Muhammad
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22596

Abstract

Unmanned Aerial Vehicles (UAVs), or drones, have recently become transformative tools in disaster management. This paper provides an overview of the role of drones in dis- aster response and recovery, covering natural disasters such as earthquakes, floods, and wildfires, as well as man-made incidents like industrial accidents and humanitarian crises. UAVs offer advantages including rapid data collection, real-time situational awareness, and improved communication capabilities. Notable examples include the use of drones in the 2015 Nepal earthquake for mapping and search operations, and during the 2017 Hurricane Harvey for flood assessment and resource distribution. Advanced technologies further enhance drone capabilities; AI algorithms were used in the 2019 Mozambique cyclone to prioritize rescue operations, while thermal sensors located survivors in the 2018 Mexico earthquake. Despite these benefits, challenges such as privacy concerns, regulatory issues, and community acceptance remain. For instance, privacy issues arose during Hurricane Harvey due to aerial surveillance, and regulatory barriers delayed responses in the 2018 Indonesia earthquake. Ethical dilemmas also surface, such as balancing response urgency with privacy rights and ensuring equitable access to UAV services. The paper discusses potential solutions, including establishing privacy protocols, engaging communities, and streamlining regulations. Collaboration between government agencies, NGOs, and the private sector is essential to develop standardized protocols and enhance community acceptance. By integrating AI, machine learning, and advanced sensors, drones can significantly improve disaster response efficiency. In conclusion, drones play a pivotal role in revolutionizing disaster management strategies. Ongoing advancements in drone technology offer unprecedented opportunities to enhance disaster response, ultimately mitigating human suffering and preserving critical infrastructure. This paper reviews the role of drones in disaster response and recovery efforts, covering various disaster types including natural and man-made incidents.
Techno-Economic and Environmental Analysis of an On-Grid and Off-Grid Renewable Energy Hybrid System in an Energy-Rich Rural Area: A Case in Indonesia Umam, Faikul; Wahyu, Fiki Milatul; Efendi, Mochamad Yusuf; Amir, Nizar; Gozan, Misri; Asmara, Yuli Panca
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22633

Abstract

Developing a dedicated renewable energy hybrid system is a viable option for extending access to electrical energy in energy-rich rural areas. This study conducted a feasibility analysis of using a hybrid energy system, combining solar photovoltaic, wind, and biogas, to generate electricity and meet the energy needs of the rural area. West Waru Village is selected as the case study area for this research because it has abundant renewable energy sources. The Hybrid Optimization of Multiple Energy Resources (HOMER) tools is employed for modeling and optimizing the hybrid energy system, offering a comprehensive analysis encompassing technical, economic, and environmental aspects. Furthermore, the study's findings were further analyzed through a sensitivity analysis, considering unpredictable factors such as village load consumption, solar radiation, wind speed, and biomass availability. Additionally, the study’s results reveals that the renewable energy hybrid system can meet nearly 80% of the rural area's electrical energy requirements at a cost of $0.16 per kWh, resulting in the reduction of 8.4 million kg of carbon dioxide emissions. These findings can serve as a baseline for stakeholders in developing renewable energy systems in rural areas.
Neuro-Fuzzy Controller for a Non-Linear Power Electronic DC-DC Boost Converters Al-Dabbagh, Zainab Ameer; Shneen, Salam Waley
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22690

Abstract

The current paper aims to explore the possibility of improving the performance of one of the important systems (boost DC-DC converter) in the field of electrical energy by contributing to the use of electronic power converters to provide the scheduled voltage to the loads with changing operating conditions using traditional (PID control) and expert (Neuro-fuzzy logic control) methods. Test cases are proposed to verify the possibility of improvement and the effectiveness of the system through approved measurement criteria such as improving stability, response time, efficiency, or a performance measure for overshoot and undershoot rates and rise time in addition to steady-state error through which comparison can be made to know the best between the methodology used to evaluate the performance of PID controllers and ANFIS (Adaptive Neuro-fuzzy Inference System). The current paper deals with a study of the operation of non-linear DC-DC Converters with a Neuro-Fuzzy Controller. To verify the system's effectiveness, proposed tests are conducted to simulate operation in real-time. The assumptions adopted are that the input voltage value is available from a direct current source with a voltage of (12) volts, and what is required to supply a load with a voltage ranging between (22-120) depending on the load change. The necessary calculations were made to calculate the converter parameters. The required inductance value was (160μH) and the capacitance value was (276μF). The simulation test was conducted using a model consisting of a resistive load and a step-up converter in addition to the supply source in both the open-loop and closed-loop system states. System tests were also conducted in the presence of the proposed controllers to verify the system's effectiveness.
Comprehensive Study on Detecting Multi-Class Classification of IoT Attack Using Machine Learning Methods Zhukabayeva, Tamara; Zholshiyeva, Lazzat; Ven-Tsen, Khu; Adamova, Aigul; Karabayev, Nurdaulet; Mardenov, Erik
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22819

Abstract

The proliferation of IoT devices has heightened their susceptibility to cyberattacks, particularly botnets. Conventional security methods frequently prove inadequate because of the restricted processing capabilities of IoT devices. This paper suggests utilizing machine learning methods to enhance the detection of attacks in Internet of Things (IoT) environments. The paper presents a novel approach to detect different botnet assaults on IoT devices by utilizing ML methods such as XGBoost, Random Forest, LightGBM, and Decision Tree. These algorithms were examined using the N-BaIoT dataset to classify multi-class botnet attacks and were specifically designed to accommodate the limitations of IoT devices. The technique comprises the steps of data preparation, preprocessing, classifier training, and decision-making. The algorithms achieved high detection accuracy rates: XGBoost (99.18%), Random Forest (99.20%), LGBM (99.85%), and Decision Tree (99.17%). The LGBM model demonstrated exceptional performance. The incorporation of the attack evaluation model greatly enhanced the identification of botnets in IoT networks. The paper displays the efficacy of machine learning techniques in identifying botnet assaults in IoT networks. The models generated exhibit exceptional accuracy and can be seamlessly integrated into existing cybersecurity systems.
Synergetic Control Design Based Sparrow Search Optimization for Tracking Control of Driven-Pendulum System Al-Khazraji, Huthaifa; Al-Badri, Kareem; Al-Majeez, Rawaa; Humaidi, Amjad J
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22893

Abstract

This study investigates the performance of designing a Synergetic Control (SC) approach for angular position tracking control of driven-pendulum systems. SC is one of the popular nonlinear control techniques that contributed in a variety of control design applications. This research shows a unique application of the SC for angular position tracking control of driven-pendulum systems. Initially, the equations of motion of the system are developed. Subsequently, the control law of the SC is established. For the stability analysis of the closed loop control system, the Lyapunov Function (L.F) is used. To guarantee optimal performance, a Sparrow Search Optimization (SSO) based approach is presented in order to search for the optimum designing parameters of the controller. For performance comparison, the classical Sliding Mode Control (SMC) is introduced. The simulation's outcomes of the study have been confirmed that the proposed control algorithm is addressed the tracking problem of the angular position of the system successfully. Besides, when an external disturbance is inherited in the simulation, the SC exhibits a robustness performance. Moreover, the performance of the SC is slightly similar as SMC. However, the distinct difference in the performance is that the control signal of the SMC exhibits chattering problem, while this phenomenon is absent in the SC. All computer simulations are carried out using MATLAB software.

Page 59 of 71 | Total Record : 708

 57   58   59   60   61 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 5 (2025) Vol. 6 No. 4 (2025) Vol. 6 No. 2 (2025) Vol. 6 No. 1 (2025) Vol 5, No 6 (2024) Vol. 5 No. 6 (2024) Vol. 5 No. 5 (2024) Vol 5, No 5 (2024) Vol 5, No 4 (2024) Vol 5, No 3 (2024) Vol 5, No 2 (2024) Vol 5, No 1 (2024) Vol 4, No 6 (2023) Vol 4, No 5 (2023) Vol 4, No 4 (2023) Vol 4, No 3 (2023) Vol 4, No 2 (2023) Vol 4, No 1 (2023) Vol 3, No 6 (2022): November Vol 3, No 5 (2022): September Vol 3, No 4 (2022): July Vol 3, No 3 (2022): May Vol 3, No 2 (2022): March Vol 3, No 1 (2022): January Vol 2, No 6 (2021): November (Forthcoming Issue) Vol 2, No 6 (2021): November Vol 2, No 5 (2021): September Vol 2, No 5 (2021): September (Forthcoming Issue) Vol 2, No 4 (2021): July Vol 2, No 4 (2021): July (Forthcoming Issue) Vol 2, No 3 (2021): May Vol 2, No 3 (2021): May (Forthcoming Issue) Vol 2, No 2 (2021): March (Forthcoming Issue) Vol 2, No 2 (2021): March Vol 2, No 1 (2021): January Vol 1, No 6 (2020): November Vol 1, No 5 (2020): September Vol 1, No 4 (2020): July Vol 1, No 3 (2020): May Vol 1, No 2 (2020): March Vol 1, No 1 (2020): January More Issue